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Quantifying and Propagating Uncertainty in Automated Linked Data Integration

Part of the Lecture Notes in Computer Science book series (TLDKS,volume 10940)


The Web of Data consists of numerous Linked Data (LD) sources from many largely independent publishers, giving rise to the need for data integration at scale. To address data integration at scale, automation can provide candidate integrations that underpin a pay-as-you-go approach. However, automated approaches need: (i) to operate across several data integration steps; (ii) to build on diverse sources of evidence; and (iii) to contend with uncertainty. This paper describes the construction of probabilistic models that yield degrees of belief both on the equivalence of real-world concepts, and on the ability of mapping expressions to return correct results. The paper shows how such models can underpin a Bayesian approach to assimilating different forms of evidence: syntactic (in the form of similarity scores derived by string-based matchers), semantic (in the form of semantic annotations stemming from LD vocabularies), and internal in the form of fitness values for candidate mappings. The paper presents an empirical evaluation of the methodology described with respect to equivalence and correctness judgements made by human experts. Experimental evaluation confirms that the proposed Bayesian methodology is suitable as a generic, principled approach for quantifying and assimilating different pieces of evidence throughout the various phases of an automated data integration process.


  • Probabilistic modelling
  • Bayesian updating
  • Data integration
  • Linked Data

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    One well-known example portal is the so-called Linked Open Data (LOD) cloud, at

  2. 2.

    For schema-less sources (e.g., Linked Data sources) schema extraction techniques can be used to infer schemas (e.g., [5]).

  3. 3.

  4. 4.

  5. 5.

  6. 6.

    A Gaussian kernel was used due to its mathematical convenience. Note that any other kernel can be applied. Of course, the shape of the distribution may differ depending on the kernel characteristics.

  7. 7.

  8. 8.

    Informally, the d.o.b., in the hypothesis given the evidence (the so-called posterior d.o.b.) is equal to the ratio between the product of the d.o.b. in the evidence given the hypothesis (which we call likelihood in Sect. 3) and the d.o.b. in the hypothesis (the so-called prior d.o.b.) divided by the d.o.b. in the evidence.

  9. 9.

  10. 10.

  11. 11.

    BLOOMS was configured with a high threshold, viz., >0.8.

  12. 12.

    We observe once more that, in this paper, the experiments have only used LD datasets but dataspaces are meant to be model-agnostic and, in particular, DSToolkit is. DSToolkit is no longer being actively developed but requests for access to the sources can be sent to the second author. The datasets used are publicly available in the LOD cloud.


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Fernando R. Sanchez S. is supported by a grant from the Mexican National Council for Science and Technology (CONACyT).

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Correspondence to Klitos Christodoulou .

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Christodoulou, K., Serrano, F.R.S., Fernandes, A.A.A., Paton, N.W. (2018). Quantifying and Propagating Uncertainty in Automated Linked Data Integration. In: Hameurlain, A., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVII. Lecture Notes in Computer Science(), vol 10940. Springer, Berlin, Heidelberg.

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